scholarly journals Computer Tools to Analyze Lung CT Changes after Radiotherapy

2021 ◽  
Vol 11 (4) ◽  
pp. 1582
Author(s):  
Marek Konkol ◽  
Konrad Śniatała ◽  
Paweł Śniatała ◽  
Szymon Wilk ◽  
Beata Baczyńska ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Digital Imaging ◽  
Input Data ◽  
Ct Images ◽  
Comprehensive Analysis ◽  
Distribution Models ◽  
Computer Tool ◽  
Computer Tools ◽  
Lung Ct

The paper describes a computer tool dedicated to the comprehensive analysis of lung changes in computed tomography (CT) images. The correlation between the dose delivered during radiotherapy and pulmonary fibrosis is offered as an example analysis. The input data, in DICOM (Digital Imaging and Communications in Medicine) format, is provided from CT images and dose distribution models of patients. The CT images are processed using convolution neural networks, and next, the selected slices go through the segmentation and registration algorithms. The results of the analysis are visualized in graphical format and also in numerical parameters calculated based on the images analysis.

Automated vessel segmentation in lung CT and CTA images via deep neural networks

2021 ◽  
pp. 1-15
Author(s):  
Wenjun Tan ◽  
Luyu Zhou ◽  
Xiaoshuo Li ◽  
Xiaoyu Yang ◽  
Yufei Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Vessel Segmentation ◽  
Multi Scale ◽  
Vascular Segmentation ◽  
Study Results ◽  
Lung Ct

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation ratio and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

scholarly journals Multi-Reader–Multi-Split Annotation of Emphysema in Computed Tomography

2020 ◽  
Vol 33 (5) ◽  
pp. 1185-1193
Author(s):  
Mats Lidén ◽  
Ola Hjelmgren ◽  
Jenny Vikgren ◽  
Per Thunberg
Keyword(s):  
Computed Tomography ◽  
Reading Time ◽  
Reliability And Validity ◽  
Image Data ◽  
Limited Resource ◽  
Apical Part ◽  
Ct Images ◽  
Observer Reliability ◽  
Split Method ◽  
Lung Ct

Abstract Emphysema is visible on computed tomography (CT) as low-density lesions representing the destruction of the pulmonary alveoli. To train a machine learning model on the emphysema extent in CT images, labeled image data is needed. The provision of these labels requires trained readers, who are a limited resource. The purpose of the study was to test the reading time, inter-observer reliability and validity of the multi-reader–multi-split method for acquiring CT image labels from radiologists. The approximately 500 slices of each stack of lung CT images were split into 1-cm chunks, with 17 thin axial slices per chunk. The chunks were randomly distributed to 26 readers, radiologists and radiology residents. Each chunk was given a quick score concerning emphysema type and severity in the left and right lung separately. A cohort of 102 subjects, with varying degrees of visible emphysema in the lung CT images, was selected from the SCAPIS pilot, performed in 2012 in Gothenburg, Sweden. In total, the readers created 9050 labels for 2881 chunks. Image labels were compared with regional annotations already provided at the SCAPIS pilot inclusion. The median reading time per chunk was 15 s. The inter-observer Krippendorff’s alpha was 0.40 and 0.53 for emphysema type and score, respectively, and higher in the apical part than in the basal part of the lungs. The multi-split emphysema scores were generally consistent with regional annotations. In conclusion, the multi-reader–multi-split method provided reasonably valid image labels, with an estimation of the inter-observer reliability.

scholarly journals Extension of Collagen Deposition in COVID-19 Post Mortem Lung Samples and Computed Tomography Analysis Findings

2021 ◽  
Vol 22 (14) ◽  
pp. 7498
Author(s):  
Lorenzo Ball ◽  
Emanuela Barisione ◽  
Luca Mastracci ◽  
Michela Campora ◽  
Delfina Costa ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Invasive Mechanical Ventilation ◽  
Ct Images ◽  
Common Finding ◽  
Collagen Deposition ◽  
Qualitative And Quantitative ◽  
Ct Findings ◽  
Radiographic Score ◽  
Quantitative Analyses ◽  
Lung Ct

Lung fibrosis has specific computed tomography (CT) findings and represents a common finding in advanced COVID-19 pneumonia whose reversibility has been poorly investigated. The aim of this study was to quantify the extension of collagen deposition and aeration in postmortem cryobiopsies of critically ill COVID-19 patients and to describe the correlations with qualitative and quantitative analyses of lung CT. Postmortem transbronchial cryobiopsy samples were obtained, formalin fixed, paraffin embedded and stained with Sirius red to quantify collagen deposition, defining fibrotic samples as those with collagen deposition above 10%. Lung CT images were analyzed qualitatively with a radiographic score and quantitatively with computer-based analysis at the lobe level. Thirty samples from 10 patients with COVID-19 pneumonia deceased during invasive mechanical ventilation were included in this study. The median [interquartile range] percent collagen extension was 6.8% (4.6–16.2%). In fibrotic compared to nonfibrotic samples, the qualitative score was higher (260 (250–290) vs. 190 (120–270), p = 0.036) while the gas fraction was lower (0.46 (0.32–0.47) vs. 0.59 (0.37–0.68), p = 0.047). A radiographic score above 230 had 100% sensitivity (95% confidence interval, CI: 66.4% to 100%) and 66.7% specificity (95% CI: 41.0% to 92.3%) to detect fibrotic samples, while a gas fraction below 0.57 had 100% sensitivity (95% CI: 66.4% to 100%) and 57.1% specificity (95% CI: 26.3% to 88.0%). In COVID-19 pneumonia, qualitative and quantitative analyses of lung CT images have high sensitivity but moderate to low specificity to detect histopathological fibrosis. Pseudofibrotic CT findings do not always correspond to increased collagen deposition.

scholarly journals Segmentation of Lung Ct Images using Cascaded Fully Convolutional Neural Networks

2019 ◽  
Vol 8 (2) ◽  
pp. 5472-5474
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Fully Convolutional Neural Networks ◽  
Proposal A ◽  
Fully Automatic ◽  
Lung Ct

Interpretation of CT Lung images by the radiologist can be enhanced to a greater extent by automatic segmentation of nodules. The efficiency of this interpretation depends on the completeness and non-ambiguousness of the CT Lung images. Here, a fully automatic cascaded basis was proposed for CT Lung image segmentation. In this proposal a customized FCN was used feature extractions exploration from many visual scales and differentiate anatomy with a thick forecast map. Widespread experimental outcomes demonstrate that this technique can address the incompleteness in boundary and this technique can achieve best accuracy in segmentation of Lung CT Images when compared to other techniques which address the same area

scholarly journals Segmentation of Lung Images using Region based Neural Networks

2018 ◽  
Vol 11 (4) ◽  
pp. 2037-2042 ◽  
Author(s):  
Z. Faizal Khan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Tomography ◽  
Region Growing ◽  
Ct Images ◽  
Second Step ◽  
Lung Region ◽  
Segmentation Accuracy ◽  
Segmentation Approach ◽  
Lung Ct

In this article, a neural network-based segmentation approach for CT lung images was proposed using the combination of Neural Networks and region growing which combines the regions of different pixels. The proposed approach expresses a method for segmenting the lung region from lung Computer Tomography (CT) images. This method is proposed to obtain an optimal segmented region. The first step begins by the process of finding the area which represents the lung region. In order to achieve this, the regions of all the pixel present in the entire image is grown. Second step is, the grown region values are given as input to the Echo state neural networks in order to obtain the segmented lung region. The proposed algorithm is trained and tested for 1,361 CT lung slices for the process of evaluating segmentation accuracy. An average of 98.50% is obtained as the segmentation accuracy for the input lung CT images.

scholarly journals A New Radiomic Study on Lung CT Images of Patients with Covid-19 using LBP and Deep Learning (Convolutional Neural Networks (CNN))

2020 ◽  
Author(s):  
Hüseyin Yaşar ◽  
Murat Ceylan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Ct Images ◽  
Gray Level ◽  
Speed Up ◽  
Rgb Images ◽  
Short Time ◽  
Lung Ct

Abstract The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease, pneumonia develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs were revealed by the help of lung CT imaging. In this study, the amount of 25 lung CT images in total (15 of Covid-19 patients and 10 of normal) was multiplied (250 images in total) using three data augmentation methods which relate to contrast change, brightness change and noise addition, and these images were subjected to automatic classification. Within the scope of the study, experiments were made for each case which include the use of the CT images of lungs (gray-level and RGB) directly, the images obtained by applying Local Binary Pattern (LBP) to these images (gray-level and RGB) and the images obtained by combining these images (gray-level and RGB). In the study, a 23-layer Convolutional Neural Networks (CNN) architecture was developed and used in classification processes. Leave-one-group-out cross validation method was used to test the proposed system. In this context, the result of the study indicated that the best AUC and EER values were obtained for the combination of original (RGB) and LBP applied (RGB) images, and these figures are 0,9811 and 0,0445 respectively. It was observed that, applying LBP to images, the use of CNN input causes an increase in sensitivity values while it causes a decrease in values of specificity. The highest sensitivity was obtained for the case of using LBP-applied (RGB) images and has a value of 0,9947. Within the scope of the study, the highest values of specificity and accuracy were obtained by the help of CT of lungs (gray-level) with 0,9120 and 95,32%, respectively. The results of the study indicate that analyzing images of lung CT using deep learning methods in diagnosing Covid-19 disease will speed up the diagnosis and significantly reduce the burden on healthcare workers.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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